Ablation Studies.
We now analyze the effectiveness of each loss term in Eq. (REF ).
We compare with four variants of TranSVAE, each removing one loss term by equivalently setting the weight \(\lambda \)  to 0.
The ablation results on UCF-HMDB, Jester, and Epic-Kitchens are shown in Tab. REF .
As can be seen, removing \(\mathcal {L}_{\text{cls}}\)  significantly reduces the transfer performance in all the tasks.
This is reasonable as \(\mathcal {L}_{\text{cls}}\)  is used to discover the discriminative features.
Removing any of \(\mathcal {L}_{\text{adv}}\) , \(\mathcal {L}_{\text{mi}}\) , and \(\mathcal {L}_{\text{ctc}}\)  leads to an inferior result than the full TranSVAE setup.
This demonstrates that every proposed loss matters in our framework.
We further conduct another ablation study by sequentially integrating \(\mathcal {L}_{\text{cls}}\) , \(\mathcal {L}_{\text{adv}}\) , \(\mathcal {L}_{\text{mi}}\) , and \(\mathcal {L}_{\text{ctc}}\)  into our sequential VAE structure.
We follow the above integration order based on the average positive improvement that a loss brings to TranSVAE as shown in Tab. REF .
We further use t-SNE [1]} to visualize the features learned by these different variants.
We plot two sets of t-SNE figures, one using the class-wise label and another using the domain label.
Fig. REF  and Fig. REF  show the visualization and the quantitative results.
As can be seen from the t-SNE feature visualizations, adding a new component improves both the domain and semantic alignments, and the best alignment is achieved when all the components are considered.
The quantitative results further show that the transfer performance gradually increases with the sequential integration of the four components, which again verifies the effectiveness of each component in TranSVAE.
